Analysis of computed tomography (CT) image volumes by clinicians, such as radiologists, calls for co-registration of the image volumes. Particularly, in oncology studies, it is desirable to co-register longitudinal image volumes having disparate coverage areas. Accordingly, three-dimensional (3D) image volume registration is an essential feature for diagnosis, therapy, and treatment follow-up when dealing with longitudinal (i.e., multi-time point) oncology studies. The use of multi-modality radiological images, such as, but not limited to, CT images, magnetic resonance (MR) images, positron emission tomography (PET) images, and/or ultrasound (US) images, as well as histopathological images makes this task extremely challenging. The success of most registration algorithms is dependent on a key assumption of significant overlap between image volumes. Unfortunately, this assumption may not be satisfied for oncology studies where significant amounts of field-of-view (FOV) or coverage mismatch may occur between multi-time point image volumes. This mismatch may be a result of modality (e.g., long acquisition time for magnetic resonance imaging (MRI)) or physiological (e.g., dose related issues for CT) limitations. A manual mismatch correction prior to the main registration of the image volumes is an onerous task due to the sheer number of exams (i.e., image volumes) that need to be co-registered.
Certain presently available techniques attempt to address this problem of mismatch correction by building an all-pair cost matrix between slices in the two (i.e., reference and target) image volumes. Each element of this cost matrix is typically computed by a weighted sum of a non-parametric Minkowski distance and a deformable displacement between pre-segmented regions of interest (ROIs) in both slices. Unfortunately, estimation of a minimum-cost straight line that determines the best matching zone of both image volumes along the axial direction entails an exhaustive search of this matrix. Other techniques for solving the mismatch problem account for invariant features in both image volumes and generate one-dimensional (1D) energy profiles that are subsequently matched with a model 1D profile. However, use of these techniques calls may produce erroneous results.
Additionally, other currently available techniques have attempted to address the mismatch correction problem by implementing a 1D histogram matching of slices followed by explicit two-dimensional (2D)-2D image registration between slices, thereby resulting in a laborious and expensive process. Certain other presently available techniques entail use of an additional step of image segmentation to identify corresponding anatomies in the two image volumes and the image volumes are registered based on relative difference in position. However, these techniques are disadvantageously dependent on robust image segmentation methods, which have a tendency to fail for partial coverage of organs and/or anatomy in the relevant volumes.